American Journal of Epidemiology Advance Access published July 3, 2014
American Journal of Epidemiology
© The Author 2014. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of
Public Health. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.
DOI: 10.1093/aje/kwu128
Original Contribution
Individual Joblessness, Contextual Unemployment, and Mortality Risk
José A. Tapia Granados*, James S. House, Edward L. Ionides, Sarah Burgard, and
Robert S. Schoeni
Initially submitted December 3, 2013; accepted for publication April 28, 2014.
Longitudinal studies at the level of individuals find that employees who lose their jobs are at increased risk of
death. However, analyses of aggregate data find that as unemployment rates increase during recessions, population mortality actually declines. We addressed this paradox by using data from the US Department of Labor and
annual survey data (1979–1997) from a nationally representative longitudinal study of individuals—the Panel
Study of Income Dynamics. Using proportional hazards (Cox) regression, we analyzed how the hazard of death
depended on 1) individual joblessness and 2) state unemployment rates, as indicators of contextual economic
conditions. We found that 1) compared with the employed, for the unemployed the hazard of death was increased
by an amount equivalent to 10 extra years of age, and 2) each percentage-point increase in the state unemployment
rate reduced the mortality hazard in all individuals by an amount equivalent to a reduction of 1 year of age. Our
results provide evidence that 1) joblessness strongly and significantly raises the risk of death among those suffering
it, and 2) periods of higher unemployment rates, that is, recessions, are associated with a moderate but significant
reduction in the risk of death among the entire population.
business cycles; Cox model; macroeconomic conditions; mortality; proportional hazards model; recessions;
unemployment
Abbreviation: PSID, Panel Study of Income Dynamics.
recessions coincide with lower mortality, while economic expansions coincide with higher mortality, so that mortality
fluctuates with the business cycle, procyclically. This procyclical fluctuation has been found for total and cause-specific
death rates, including cardiovascular and infectious disease
mortality, as well as for traffic deaths and industrial injuries
(16). The major exception is suicide mortality, which rises
during recessions and falls during expansions, oscillating
countercyclically (12–14, 17).
Contextual unemployment may be an indicator of changes
in the economy that modify the present risk of death for all
individuals. During economic expansions, firms hire new
workers, traffic volume rises, commuting times increase,
working hours rise, and workers are given more overtime
in response to the increased demand—which may increase
stress at work (18, 19). Increased industrial activity and transportation raise levels of atmospheric pollution (20), and
Job loss has been repeatedly found to be associated with
psychological distress and higher risks of disease and death
(1–4). Increased mortality among the unemployed may be
mediated by reduced income, disrupted social ties, feelings
of hopelessness or worthlessness, and difficulties in meeting
financial obligations, leading to depression, substance abuse,
or other harmful conditions and behaviors (1–3, 5–11).
Harmful effects of individual unemployment might take years
to develop, but in mass layoffs it has been observed that to a
large extent these effects occur during the first year following
the loss of the job (3).
Paradoxically, when the association between unemployment rates and mortality is modeled at the aggregate level
using longitudinal analyses, which adjust for spatial and temporal influences, the opposite association appears: higher unemployment rates correlate with lower mortality (12–15).
This means that over and above long-term trends in mortality,
1
Downloaded from http://aje.oxfordjournals.org/ at Drexel Hahnemann Library on July 8, 2014
* Correspondence to Dr. José A. Tapia Granados, 3021-E MacAlister Hall, Department of History and Political Science, Drexel University,
3250-60 Chestnut Street, Philadelphia, PA 19104 (e-mail: jat368@drexel.edu).
2 Tapia Granados et al.
consumption and time-use patterns change (21–24). Overworked persons may provide less care to others—which
can be harmful for both caregivers and those who miss out
on care (25)—and may sleep less (23), which can also be
health-damaging. More frequent commuting and migration
during expansions enhance germ transmission and raise the
risk of transportation injuries. These and other changes
have been hypothesized as mediators of the rise in mortality
rates observed during economic upturns (12, 13, 26–36).
In this study, we attempted to understand the paradox of
higher mortality risk associated both with individual unemployment and with lower contextual unemployment rates
by using—for the first time, to our knowledge—an analysis
that allowed us to estimate both the individual and aggregatelevel associations simultaneously.
We analyzed a data set constructed by merging individuallevel data from the Panel Study of Income Dynamics (PSID)
with state-level data on macroeconomic contextual conditions from the US Department of Labor for the residential locations of PSID respondents.
The PSID, which has been used in numerous studies of
health outcomes (4, 37–39), is the longest-running longitudinal household survey of US citizens. It started with persons
living in 5,000 families in 1968. Information on the original
respondents and their descendants was obtained via annual
interviews through 1997 and via biannual interviews thereafter. African Americans were oversampled. The initial sample
was clustered and stratified. Weights for each person-year
were developed by PSID staff to keep the sample representative of the US adult population by accounting for differential
selection and differential attrition over time. We used these
weights in our regressions. Deaths were identified by record
linkage with the National Death Index.
To be able to use lagged variables and to avoid problems
related to having surveys administered only every other year,
we used the 1979–1997 sample of household heads and
spouses, for which data for all years are available and all deaths
of PSID participants are identified. This sample comprised
156,357 observations. However, many of these observations
had missing information for some of the variables—age,
sex, race, marital status, educational level, household income,
employment status—considered in our analyses. The sample
we used included only the observations for which complete information was available (see Web Table 1, available at http://
aje.oxfordjournals.org/). The sample had 142,927 observations corresponding to 12,558 individuals and 1,694 deaths.
There were 11.4 observations per individual (standard deviation, 6.3). Mean age at first interview was 36.2 years (standard
deviation, 16.6). The proportion of participants with less than a
high school education (36.5% in 1979) consistently decreased
over follow-up. Each year approximately 1% of the persons in
the sample died, which is a crude death rate in the usual range
for an adult population.
PSID respondents were asked whether they were “working
now,” “only temporarily laid off, sick leave or maternity
leave,” “looking for work, unemployed,” “retired,” “temporarily or permanently disabled,” “keeping house,” “student,”
ln hist ¼ αt þ β0 Uys þ β1 Eiys þ β2 x2iys þ þ βk xkiys þ δs þ γy ;
where αt is a function of continuous time t; Uys is the unemployment rate in year y for state s; Eiys is the employment
status of individual i in year y in state s; β0–βk are the effect
estimates of contextual unemployment (β0), individual employment status (β1), and other covariates x2iys–xkiys that
may have an effect on health (marital status, educational
level, income); and δs and γy are fixed effects for state s
and year y, respectively. The covariates can be individual
time-invariant factors (in which case xkys is equal for all y),
such as sex or race, or time-variant factors such as income
or employment status. Omitting subindices for state and
year, for any covariate x the ratio of hazard rates for 2 observations p and q is
hp
¼ exp[βðxp xq Þ:
hq
The hazard ratio is hp /hq when xp − xq = 1, that is, when the
variable increases by 1 unit so that hp /hq = exp(β).
These Cox models yield estimates of how the mortality
hazard—generally interpreted as an instantaneous probability of death—changes in association with both the individual
experience of joblessness and the contextual unemployment
rate, with adjustment for the other covariates. State and year
fixed effects adjust for omitted variables that are either stateinvariant or time-invariant. We used a sandwich estimator to
compute robust standard errors for repeated observations.
Thus, we measured unemployment at 2 levels (individual
and aggregate) and estimated its statistical effects on the mortality hazard accounting for the fact that measurements of
units within a cluster are more similar than measurements of
units in different clusters. In this sense (42, 43), our approach
can be considered a multilevel analysis.
All regression models included marital status, educational
level, and income as time-varying covariates. For the present investigation, each individual was designated simply as
married or nonmarried, with nonmarried used as the reference
category. Educational level was included as a categorical variable with 3 categories: no high school education (the reference category), some high school education, and high school
diploma or higher education. Real household income was
measured in 1990 dollars and attributed to each of the individuals in the household.
In contrast to mechanisms mediating the health effects of
macroeconomic conditions, many of which are likely to be
quite contemporaneous (e.g., fewer traffic accidents and
lower levels of atmospheric pollution in recessions, as well
as decreased levels of overtime, increased sleep, or reduced
Downloaded from http://aje.oxfordjournals.org/ at Drexel Hahnemann Library on July 8, 2014
METHODS
or “other, ‘workfare,’ in prison or jail.” We considered as unemployed those persons who stated that they were “looking
for work, unemployed.”
We conducted Cox proportional hazards regressions (40,
41) in which the hazard of death at time t in year y (y is the
integer part of t) for individual i in state s (hist) was modeled
as follows:
Joblessness, Contextual Unemployment, and Mortality Risk 3
Table 1. Hazard Ratios Obtained in Cox Regressions in Which the Hazard of Death was Modeled as a Function
of Year and State Fixed Effects, the State Unemployment Rate, and the Individual’s Age, Sex, Race, Marital Status,
Educational Level, Family Income, and Respondent Employment Status, Panel Study of Income Dynamics,
1979–1997a
Explanatory Variable
Unemployment
Effect at the
Individual Level
Only: Model M1
HR
95% CI
Unemployment
Effect at the Contextual
(State) Level
Only: Model M2
HR
95% CI
Unemployment Effects
at Both the Individual
Level and the Contextual
(State) Level: Model M3
HR
95% CI
Age, years
1.07
1.07, 1.08
1.08
1.08, 1.09
1.07
1.07, 1.08
Female sex
0.48
0.41, 0.56
0.46
0.41, 0.53
0.48
0.41, 0.56
Racial/ethnic minorityb
1.26
1.04, 1.53
1.26
1.03, 1.53
1.25
1.03, 1.52
Marriedc
0.64
0.55, 0.74
0.66
0.57, 0.77
0.64
0.55, 0.74
Educationc
0.85
0.71, 1.02
0.84
0.70, 1.01
0.85
0.71, 1.02
Some high school
1.07
0.92, 1.23
1.06
0.92, 1.23
1.06
0.92, 1.23
0.87
0.66, 1.16
0.78
0.55, 1.11
0.87
0.65, 1.16
Incomed
Employment status
Unemployed
1.73
1.01, 2.96
1.77
1.05, 2.98
Student
0.69
0.24, 1.96
0.69
0.24, 1.96
Retired
1.77
1.44, 2.18
1.77
1.44, 2.17
Keeping house
1.40
1.09, 1.80
State unemployment ratee
0.91
0.86, 0.96
1.39
1.09, 1.79
0.91
0.86, 0.96
Abbreviations: CI, confidence interval; HR, hazard ratio; PSID, Panel Study of Income Dynamics.
a
Wald 95% CIs were based on robust standard errors for repeated observations in each individual, computed with a
sandwich estimator. Times of interview and death were computed with a monthly approximation, ties were handled by
the Efron method, and all models converged appropriately. Deaths were assumed to have occurred in the state in which
the individual was living during the last interview, but results changed very little after dropping that assumption by
making the state of the last interview equal to the state where death occurred (when this was known and both
differed). The models included 142,699 observations and 1,635 deaths.
b
Any answer other than “white” to the PSID question on race/ethnicity.
c
The reference category for marital status (a dichotomous variable) was “not married,” and for education (which
had 3 categories) it was “no high school education.”
d
Total annual household income in hundred thousand dollars (at 1990 prices).
e
Measured as a percentage of the economically active population, so the hazard ratio corresponds to the increase
in the hazard associated with a 1-percentage-point increase in the unemployment rate.
smoking), effects of individual job loss may take time to
affect mortality, though they may also arise just weeks or
months following job loss (3). In an annual time frame, a contemporaneous association between individual unemployment and death may imply causation in both directions,
because the onset of disease and death might be caused by
joblessness or unemployment might be caused by ill health
that then in turn causes death. However, the direction of causality from bad health to unemployment is less likely in models in which unemployed status is observed with a lag of 1 or
2 years. Therefore, we fitted models to estimate 1) the association of unemployment rates and individual employment
status with the hazard of death during the same year (Table 1);
2) the lagged association of the individual’s employment status in the past year with the hazard of death in the current year
(Table 2); and 3) the lagged macroeconomic effects ( proxied
by the unemployment rate lagged 1 year) on the hazard of
death (Table 3). To help control for the possibility of reverse
causality from bad health to joblessness, we also fitted
4) lagged expanded models (Table 3) including different
combinations of covariates lagged 1 year, as well as an index
of limitations to work, which can be considered a rough index
of physical health. Finally, we fitted 5) general expanded
models including in the regression the major explanatory variables at lags 0, 1, and 2 years (Table 4).
We fitted some models (Table 1) considering the 5 major
categories of employment status (i.e., employed, unemployed,
student, retired, and keeping house). However, since we were
particularly interested in the effects of individual joblessness or contextual unemployment, in most of our models
(Tables 2–4) the sample just included observations of persons who were at risk of both exposures because they were
either employed or unemployed.
RESULTS
When modeling the hazard of death as a function of
age, sex, race, marital status, education, income, and different
Downloaded from http://aje.oxfordjournals.org/ at Drexel Hahnemann Library on July 8, 2014
High school diploma or more
4 Tapia Granados et al.
Table 2. Hazard Ratios Obtained in Cox Regressions in Which the Hazard of Death was Modeled as a Function of Year and State Fixed Effects,
the State Unemployment Rate, and the Individual’s Age, Sex, Race, Marital Status, Educational Level, Family Income, and Employment Status,
Panel Study of Income Dynamics, 1979–1997a
Explanatory Variableb
Unemployment
Effect at the
Individual Level
Only: Model M1L
Unemployment Effect
at the Contextual
(State) Level
Only: Model M2L
Unemployment Effects at Both the Individual Level
and the Contextual (State) Level
Model M3L
Model M4L
Model M5L
HR
95% CI
HR
95% CI
HR
95% CI
HR
95% CI
HR
95% CI
Age, years
1.07
1.05, 1.08
1.06
1.05, 1.08
1.06
1.05, 1.08
1.06
1.05, 1.08
1.06
1.05, 1.08
Female sex
0.37
0.26, 0.52
0.37
0.26, 0.52
0.37
0.26, 0.52
0.37
0.27, 0.52
0.37
0.27, 0.52
Racial/ethnic minority
1.70
1.17, 2.47
1.66
1.14, 2.41
1.63
1.12, 2.36
1.63
1.12, 2.36
1.65
1.14, 2.41
Married
0.47
0.33, 0.66
0.47
0.33, 0.66
0.48
0.34, 0.68
0.48
0.34, 0.68
0.48
0.34, 0.68
Some high school
0.80
0.54, 1.20
0.80
0.54, 1.19
0.84
0.56, 1.24
0.83
0.56, 1.24
0.83
0.56, 1.23
High school diploma or more
1.09
0.74, 1.61
1.05
0.71, 1.54
1.09
0.74, 1.59
1.08
0.74, 1.59
1.08
0.74, 1.57
0.94
0.66, 1.33
0.91
0.62, 1.33
0.93
0.65, 1.32
0.93
0.65, 1.32
0.92
0.64, 1.32
0.82
0.47, 1.44
2.16
1.32, 3.54
2.20
1.34, 3.62
2.19
1.33, 3.61
2.28
1.37, 3.80
0.80
0.69, 0.93
Education
Unemployed status
Lag 0 years
Lag 1 year
State unemployment rate
Lag 0 years
0.81
0.70, 0.94
Lag 1 year
0.78
0.63, 0.97
0.78
0.62, 0.97
1.04
0.83, 1.29
1.04
0.83, 1.31
Abbreviations: CI, confidence interval; HR, hazard ratio.
a
The effects of both individual employment and contextual (state) unemployment rates were considered at lags of 0 and 1 years, and the sample
was restricted to observations for either employed or unemployed respondents. Since the models included lagged values, the first observation in each
individual was discarded. The models included 84,480 observations and 346 deaths.
b
All specifications of variables were as shown in Table 1.
Table 3. Hazard Ratios Obtained in the Same Models as Those Shown in Table 2, Expanded by Including an Index of Work Limitations Lagged
1 Year, Panel Study of Income Dynamics, 1979–1997a
Explanatory Variableb
Unemployment
Effect at the
Individual Level
Only: Model M1LE
Unemployment Effect
at the Contextual
(State) Level
Only: Model M2LE
Unemployment Effects at Both the Individual Level
and the Contextual (State) Level
Model M3LE
Model M4LE
HR
95% CI
HR
95% CI
HR
95% CI
HR
95% CI
Age, years
1.06
1.05, 1.08
1.06
1.05, 1.07
1.06
1.05, 1.08
1.06
1.05, 1.08
Female sex
0.38
0.27, 0.54
0.38
0.27, 0.54
0.38
0.27, 0.54
0.38
0.27, 0.54
Racial/ethnic minority
1.72
1.19, 2.50
1.69
1.17, 2.45
1.66
1.15, 2.40
1.66
1.14, 2.39
Married
0.48
0.34, 0.67
0.47
0.33, 0.67
0.49
0.34, 0.68
0.49
0.34, 0.69
Some high school
1.13
0.76, 1.67
1.08
0.73, 1.60
1.12
0.76, 1.65
1.12
0.76, 1.64
High school diploma or more
0.84
0.56, 1.26
0.84
0.56, 1.25
0.87
0.58, 1.30
0.87
0.58, 1.30
Income, lag 1 year
0.95
0.68, 1.33
0.92
0.64, 1.33
0.94
0.67, 1.31
0.94
0.67, 1.32
Unemployed status, lag 1 year
2.16
1.32, 3.54
2.09
1.27, 3.46
2.09
1.26, 3.45
0.80
0.69, 0.93
Education
State unemployment rate
Lag 0 years
0.80
0.69, 0.93
Lag 1 year
Work limitations,c lag 1 year
1.26
1.03, 1.55
1.29
1.05, 1.57
1.26
1.03, 1.55
0.78
0.63, 0.98
1.03
0.82, 1.29
1.26
1.03, 1.54
Abbreviations: CI, confidence interval; HR, hazard ratio.
a
The models included 82,143 observations and 343 deaths.
b
All specifications of variables were as shown in Table 1.
c
Categorical variable with 4 levels referring to the absence (0) or presence of some condition limiting “a lot” (3), “somewhat” (2), or “just a little”
(1) the amount of work the respondent could do. The hazard ratio represents the increase in mortality risk associated with a 1-unit increase in this
variable.
Downloaded from http://aje.oxfordjournals.org/ at Drexel Hahnemann Library on July 8, 2014
Income, lag 1 year
Joblessness, Contextual Unemployment, and Mortality Risk 5
Table 4. Hazard Ratios Obtained From Cox Regressions in Which
the Mortality Hazard Was a Function of Indicators of Individual or
Contextual Economic Conditions (as in Former Tables) and Other
Covariates at Lags 0, 1, and 2 Years, Panel Study of Income
Dynamics, 1979–1997a
Explanatory Variable
Model L2-A
Model L2-B
HR
95% CI
HR
95% CI
Age, years
1.07
1.05, 1.08
1.07
1.05, 1.08
Female sex
0.42
0.29, 0.60
0.42
0.29, 0.60
1.57
1.06, 2.32
1.57
1.06, 2.31
Married
0.46
0.31, 0.66
0.46
0.32, 0.66
Some high school
1.30
0.87, 1.96
1.31
0.87, 1.96
High school diploma
or more
0.96
0.63, 1.48
0.97
0.64, 1.49
Lag 0 years
1.09
0.76, 1.57
Lag 1 year
0.90
0.56, 1.43
Lag 2 years
1.06
0.80, 1.41
Lag 0 years
0.84
0.47, 1.49
0.83
0.48, 1.46
Lag 1 year
2.69
1.51, 4.78
2.67
1.50, 4.76
Lag 2 years
0.75
0.36, 1.57
0.75
0.36, 1.58
Lag 0 years
1.53
1.17, 2.01
1.53
1.17, 2.00
Lag 1 year
1.03
0.78, 1.34
1.03
0.78, 1.34
Lag 2 years
1.02
0.75, 1.39
1.02
0.75, 1.39
Lag 0 years
0.84
0.64, 1.09
0.83
0.71, 0.98
Lag 1 year
0.99
0.71, 1.37
Lag 2 years
1.01
0.79, 1.30
Education
Income
Unemployed status
Limitations to work
State unemployment
rate
Measure of model fit
Akaike Information
Criterion
130,940
130,939
Schwartz Bayesian
Criterion
131,249
131,229
Abbreviations: CI, confidence interval; HR, hazard ratio.
a
The models included 72,471 observations and 306 deaths.
b
All specifications of variables were as shown in Table 1.
combinations of the individual’s employment status and the
contextual (state) unemployment rate, and considering different specifications and subsamples, the hazard of death is, as
expected, higher for older respondents, males, nonwhites,
and persons who are unmarried or have a lower level of income or education (Tables 1–4).
The hazard ratio for age in different models (Tables 1–4)
varies in a narrow range from 1.06 to 1.08, indicating that
each year of age adds approximately 7% to the hazard of
death. Females have a significantly lower hazard of death.
An increased hazard of death for nonwhites (for instance,
by 26%; hazard ratio = 1.26 in model M1, Table 1) is observed across models (Tables 2–4), but the increase in the
Downloaded from http://aje.oxfordjournals.org/ at Drexel Hahnemann Library on July 8, 2014
Racial/ethnic minority
hazard, though always significant, ranges widely from 25%
(Table 1, model M3) to 72% (Table 3, model M1LE).
Compared with nonmarried persons, married persons have
a significantly reduced hazard of death—for instance, by
36% (i.e., 1 − 0.64 = 0.36) in model M1 (Table 1). Higher
levels of education (high school diploma or more) or income
also appear to be associated with decreased mortality risk in
most models, but the 95% confidence interval for the hazard
ratio corresponding to these variables includes 1, so the association is not significant at the usual level of confidence.
Employment status appears across models as an important
and significant determinant of the hazard of death. Compared
with employed persons, the hazard of death is significantly
higher for the unemployed. In model M1 (Table 1), the hazard is raised by 73% (hazard ratio = 1.73, 95% confidence interval: 1.01, 2.96). Retired persons and persons keeping
house also have hazards of death that are significantly increased (by 77% and 40%, respectively; Table 1, model M1).
Across models, the hazard ratio for contextual unemployment is significantly below 1, indicating a reduction of the
mortality hazard when the unemployment rate is higher. In
model M2 (Table 1), each percentage-point increase in the
unemployment rate reduces the hazard of death by 9% (i.e.,
0.91 − 1 = −0.09). Since the 95% confidence interval for the
hazard ratio ranges from 0.86 to 0.96, the 95% confidence interval for the hazard reduction associated with a 1-percentage-point
increase in the unemployment rate is 4%–14%.
When model M2 is expanded by including the participant’s employment status (Table 1, model M3), the hazard
ratio for contextual unemployment does not change at all.
The hazard ratio for individual joblessness is now 1.77,
whereas it was 1.73 when contextual unemployment was
not included in the model.
In the unrestricted sample (Table 1, models M1–M3), the
unemployment rate entered into the model contemporaneously and individual joblessness raise the hazard of death
by approximately 75%. In the sample restricted to observations of employed or unemployed persons, individual joblessness lagged 1 year raises the hazard of death by a factor
of 2.2 (Table 2, models M1L, M3L, and M4L). The state unemployment rate, which reduced the hazard of death by about
9% in the unrestricted sample (Table 1), now reduces it by
19% (i.e., 0.81 − 1 = −0.19; Table 2, model M2L). This effect is mostly contemporaneous, since the state unemployment rate is not significant when lagged 1 year (Table 2,
models M4L and M5L).
Hazard ratios greater than 2 for unemployed status lagged
1 year (Table 2, models M1L and M3L–M5L) suggest a
causal effect of individual unemployed status increasing the
risk of death. The hazard ratio for individual unemployment
at lag 1 is also significantly elevated in the lagged expanded
models (Table 3, models M1LE, M3LE, and M4LE), in
which an index of work limitations is included, also with a
lag of 1 year. This provides further evidence in favor of individual joblessness raising mortality risk, since the index of
work limitations can be thought of as a measure of selfassessed health. Hazard ratio estimates for the index of
work limitations are between 1.26 and 1.29 and highly significant, so that a 1-point increase in the index raises the hazard
of death the following year by almost 30%. The inclusion of
6 Tapia Granados et al.
DISCUSSION
Our results indicate that in comparison with employed persons, the unemployed have a significantly increased hazard of
death. Since the increase in this hazard is at least 73% (Table 1,
model M1) and 1 extra year of age raises the hazard of death by
approximately 7%, the health-damaging effect associated with
being jobless is similar to the effect of about 10 extra years of
age. However, each percentage-point increase in contextual
unemployment reduces the hazard of death by approximately
9% (Table 1, model M3). The magnitude of this effect is
slightly greater than that of reducing age by 1 year.
The effects of unemployment measured at the group and
individual levels are both statistically significant, but with opposite signs: While contextual unemployment reduces the
mortality hazard, individual joblessness increases it. When
coding individual unemployment as 0 in employed persons
and 1 in the unemployed, for the sample of observations
corresponding to employed or unemployed persons (n =
100,380) the correlation between individual unemployment
and the state unemployment rate is positive and highly significant (P < 0.0001), but its value is as small as 0.07. For individual years, this correlation varies between 0.02 and 0.09,
and the highest value, 0.091, is for 1984, when the national
unemployment rate was at its highest level during the study
period. Because of the low level of correlation between
these two variables, their effect estimates remain almost identical when the other variable is included in the model.
A key issue is the extent to which these observed associations reflect causal processes. In the case of contextual unemployment, reverse causality would imply that changes in
the hazard of death cause changes in the state unemployment
rate, which does not seem credible. Therefore it must be concluded that either contextual unemployment is indeed changing
the hazard of death or, at the same time that the unemployment rate changes in the state, other processes are occurring
that change the hazard of death. Obviously, the latter seems
the most logical explanation. Potential mechanisms ( pollution, work environment, enhanced circulation of pathogens,
etc.) have been noted above.
In the association between individual joblessness and
death, causality issues are more complex. In analyses considering contemporary employment status (Table 1), the higher
hazard among the unemployed can be explained by unemployment status raising the risk of ill health and death or by
persons with poor health and increased mortality risk being at
higher risk of becoming unemployed. Both processes are
probably at work (5, 9). The results of models in which employment status is lagged 1 year (Table 2) allow less room for
bidirectional causation. Finally, the evidence of causality in
the direction from individual joblessness to death is strengthened by 1) the results of models in which the effect of unemployment lagged 1 year is adjusted for an index that to
some extent measures the level of previous health (Table 3)
and 2) the results of models in which lagged effects of joblessness at lags 0, 1, and 2 years are considered (Table 4), with adjustment for other variables, including an index of physical
health. In these models, the hazard of death depends on the
status of being unemployed measured up to 2 years earlier,
and with an adjustment for an index of limitations to work
that should adjust somewhat for previous health status 2
years earlier. These adjustments and lags do not totally exclude the possibility that unmeasured ill health increases
both the risk of being unemployed and the risk of mortality;
however, in applied statistics, lagged effects like these are
usually considered strong evidence supporting causation of
the outcome by the lagged variable (41).
While in the general population the contextual effect of the
economy—indexed by the unemployment rate—is revealed
by hazard ratios around 0.9 (Table 1), in samples restricted
to persons participating in the labor force hazard ratios are
even lower, around 0.8 (Tables 2–4). This indicates that the
contextual effect of the economic environment is greater
among persons participating in the labor force. Our study is
therefore inconsistent with the recently suggested hypothesis
that procyclical mortality is a phenomenon restricted to the
elderly population (33, 34). It is consistent, though, with results of other studies in which general mortality was found to
be fluctuating procyclically at all ages but more intensely
among young or middle-aged adults (12, 14, 15).
We used a nationally representative panel of US individuals to evaluate two facts that have been seen as inconsistent: a
harmful effect of individual joblessness and a decrease in
population mortality during recessions, when unemployment
rates rise. We have shown that the two effects co-occur and
are consistent with studies that examine them separately.
The increase in the hazard of death associated with being unemployed is very strong, but it is restricted to unemployed
persons, who generally are a small fraction of the population
even in a severe recession. Compared with the increase in the
hazard of death among the unemployed, the decrease of the
mortality risk associated with a weakening economy is small,
but the benefit spreads across the entire adult population. The
compound result of both effects is that total mortality rises
Downloaded from http://aje.oxfordjournals.org/ at Drexel Hahnemann Library on July 8, 2014
this index in the model reduces the hazard ratio for individual
unemployment, but only minimally (from 2.20 in model
M3L (Table 2) to 2.09 in model M3LE (Table 3)), and the
hazard ratios well over 2 indicate that unemployed status in
the past year is still associated with a doubling of the hazard
of death, even after adjustment for prior health.
In model L2-A (Table 4), income, limitations to work, and
individual or contextual unemployment are entered into the
model at lags 0–2. The model can be improved, however,
by eliminating some nonsignificant covariates. Thus, a better
fit (lower values for the Akaike Information Criterion and
Schwartz Bayesian Criterion) is found for model L2-B,
which excludes income and includes the unemployment
rate at lag 0 only. The fit did not improve, however, by eliminating lagged values of unemployed status or the index of
limitations to work.
In model L2-B, there are significant hazard ratios of 2.67
and 0.83 for unemployed status at lag 1 and for contextual
unemployment at lag 0, respectively. Thus, in this sample
of employed or unemployed persons, individual joblessness
is associated with an almost tripling of the hazard of death the
following year. At the same time, a percentage-point increase
in the state unemployment rate is associated with a reduction
in the hazard of death by 17% (i.e., 0.83 − 1 = −0.17) at lag 0,
without lagged effects.
Joblessness, Contextual Unemployment, and Mortality Risk 7
ACKNOWLEDGMENTS
Author affiliations: Department of History and Political
Science, College of Arts and Sciences, Drexel University,
Philadelphia, Pennsylvania (José A. Tapia Granados); and
Institute for Social Research (James S. House, Sarah Burgard,
Robert S. Schoeni), Gerald R. Ford School of Public Policy
(James S. House, Robert S. Schoeni), Department of Sociology, College of Literature, Science, and the Arts (LSA)
(James S. House, Sarah Burgard), Department of Statistics,
LSA (Edward L. Ionides), Department of Epidemiology,
School of Public Health (Sarah Burgard), Population Studies
Center (Sarah Burgard, Robert S. Schoeni), and Department of
Economics, LSA (Robert S. Schoeni), University of Michigan,
Ann Arbor, Michigan.
This investigation was partially supported by National
Institutes of Health grant HD057411-02 from the Eunice
Kennedy Shriver National Institute of Child Health and
Human Development.
We thank Patricia Andreski and Fabian Pfeffer for their
help with the data.
Conflict of interest: none declared.
REFERENCES
1. Winkelmann L, Winkelmann R. Why are the unemployed so
unhappy? Evidence from panel data. Economica. 1998;
65(257):1–15.
2. Burgard SA, Brand JE, House JS. Toward a better estimation of
the effect of job loss on health. J Health Soc Behav. 2007;48(4):
369–384.
3. Sullivan D, von Wachter T. Job displacement and mortality: an
analysis using administrative data. Q J Econ. 2009;124(3):
1265–1306.
4. Strully KW. Job loss and health in the U.S. labor market.
Demography. 2009;46(2):221–246.
5. Martikainen P, Maki N, Jantti M. The effects of unemployment
on mortality following workplace downsizing and workplace
closure: a register-based follow-up study of Finnish men and
women during economic boom and recession. Am J Epidemiol.
2007;165(9):1070–1075.
6. Bartley M, Ferrie J. Glossary: unemployment, job insecurity, and
health. J Epidemiol Community Health. 2001;55(11):776–781.
7. Bartley M. Unemployment and health selection. Lancet. 1996;
348(9032):904.
8. Martikainen PT, Valkonen T. Excess mortality of unemployed
men and women during a period of rapidly increasing
unemployment. Lancet. 1996;348(9032):909–914.
9. Valkonen T, Martikainen PT. The association between
unemployment and mortality: causation or selection? In:
Lopez AD, Casell G, Valkonen T, eds. Adult Mortality in
Developed Countries: From Description to Explanation.
Oxford, United Kingdom: Clarendon Press; 1996:1859–1861.
10. Turner JB. Economic context and the health effects of
unemployment. J Health Soc Behav. 1995;36(3):213–229.
11. Hayes J, Nutman P. Understanding the Unemployed: The
Psychological Effects of Unemployment. London, United
Kingdom: Tavistock Publications; 1981.
12. Ruhm CJ. Are recessions good for your health? Q J Econ. 2000;
115(2):617–650.
13. Tapia Granados JA, Diez Roux AV. Life and death during the
Great Depression. Proc Natl Acad Sci U S A. 2009;106(41):
17290–17295.
14. Ionides EL, Wang Z, Tapia Granados JA. Macroeconomic
effects on mortality revealed by panel analysis with nonlinear
trends. Ann Appl Stat. 2013;7(3):1362–1385.
15. Rolden HJA, van Bodegom D, van den Hout WB, et al. Old age
mortality and macroeconomic cycles. J Epidemiol Community
Health. 2014;68(1):44–50.
16. Kossoris MD. Industrial injuries and the business cycle.
Monthly Labor Rev. 1939;46(3):575–579.
17. Luo F, Florence CS, Quispe-Agnoli M, et al. Impact of business
cycles on US suicide rates, 1928–2007. Am J Public Health.
2011;101(6):1139–1146.
18. Liu Y, Tanaka H, Fukuoka Heart Study Group. Overtime work,
insufficient sleep, and risk of non-fatal acute myocardial
infarction in Japanese men. Occup Environ Med. 2002;59(7):
447–451.
19. Sokejima S, Kagamimori S. Working hours as a risk factor
for acute myocardial infarction in Japan: case-control study.
BMJ. 1998;317(7161):775–780.
20. Davis ME, Laden F, Hart JE, et al. Economic activity and trends
in ambient air pollution. Environ Health Perspect. 2010;
118(5):614–619.
21. Xu X. The business cycle and health behaviors. Soc Sci Med.
2013;77:126–136.
22. Mitchell WC. What Happens During Business Cycles:
A Progress Report. Cambridge, MA: National Bureau of
Economic Research; 1951.
23. Biddle JE, Hamermesh DS. Sleep and the allocation of time.
J Polit Econ. 1990;98(5):922–943.
24. Eyer J. Prosperity as a cause of death. Int J Health Serv. 1977;
7(1):125–150.
Downloaded from http://aje.oxfordjournals.org/ at Drexel Hahnemann Library on July 8, 2014
during economic expansions and drops during recessions, as
has been repeatedly found in recent research where investigators have not been able to study both processes concurrently
(15, 29, 44–51).
In many previous investigations, education and income
have been found to be significant predictors of mortality
(52–55). In our results, higher levels of education or income
appear to be associated with lower mortality, but the association is—somewhat surprisingly—not statistically significant. It must be considered, though, that our models simply
predict the hazard of death over a short run of 1 or 2 years
rather than over a longer time interval.
Further research is needed to establish the still-uncertain
mechanisms responsible for the phenomena highlighted in
this investigation, particularly those linking macroeconomic
fluctuations with major causes of death, such as cardiovascular disorders. A better knowledge of these mechanisms might
suggest how such major scourges of our society could be ameliorated via some combination of public policy and health
practice.
In summary, our findings show that job loss is associated
with a large increase in the hazard of death, though this increased risk affects only a minority of the population and is
outweighed by smaller (though sizable) health-promoting effects of an economic slowdown that affects the entire population. This combination of effects needs greater attention in
health research, policy, and practice.
8 Tapia Granados et al.
40. Lee ET. Statistical Methods for Survival Data Analysis.
Belmont, CA Lifetime Learning Publications; 1980:306–307.
41. Allison PD. Survival Analysis Using SAS: A Practical Guide.
2nd ed. Cary, NC: SAS Publications; 2010.
42. Fitzmaurice GM, Laird NM, Ware JH. Applied Longitudinal
Analysis. Hoboken, NJ: Wiley-Interscience; 2004.
43. Diez-Roux AV. Multilevel analysis in public health research.
Annu Rev Public Health. 2000;21:171–192.
44. Neumayer E. Recessions lower (some) mortality rates: evidence
from Germany. Soc Sci Med. 2004;58(6):1037–1047.
45. Tapia Granados JA, Ionides EL. Mortality and macroeconomic
fluctuations in contemporary Sweden. Eur J Popul. 2011;27(2):
157–184.
46. Tapia Granados JA, Ionides EL. The reversal of the relation
between economic growth and health progress: Sweden in the
19th and 20th centuries. J Health Econ. 2008;27(3):544–563.
47. Abdala F, Geldstein RN, Mychaszula SM. Economic
restructuring and mortality changes in Argentina—is there any
connection? In: Cornia GA, Paniccià R, eds. The Mortality
Crisis in Transitional Economies. New York, NY: Oxford
University Press; 2000:328–350.
48. Gonzalez F, Quast T. Macroeconomic changes and mortality in
Mexico. Empir Econ. 2011;40(2):305–319.
49. Tapia Granados JA. Recessions and mortality in Spain, 1980–
1997. Eur J Popul. 2005;21(4):393–422.
50. Tapia Granados JA. Economic growth and health progress in
England and Wales: 160 years of a changing relation. Soc Sci
Med. 2012;74(5):688–695.
51. Lin SJ. Economic fluctuations and health outcome: a panel
analysis of Asian-Pacific countries. Appl Econ. 2009;41(4):
519–530.
52. Kitagawa EM, Hauser PM. Differential Mortality in the United
States: A Study in Socioeconomic Epidemiology. Cambridge,
MA: Harvard University Press; 1973.
53. Ettner SL. New evidence on the relationship between income
and health. J Health Econ. 1996;15(1):67–85.
54. Smith JP. Healthy bodies and thick wallets: the dual relation
between health and economic status. J Econ Perspect. 1999;
13(2):144–166.
55. Isaacs SL, Schroeder SA. Class—the ignored determinant of
the nation’s health. N Engl J Med. 2004;351(11):1137–1142.
Downloaded from http://aje.oxfordjournals.org/ at Drexel Hahnemann Library on July 8, 2014
25. Roth DL, Haley WE, Hovater M, et al. Family caregiving and
all-cause mortality: findings from a population-based propensitymatched analysis. Am J Epidemiol. 2013;178(10):1571–1578.
26. Gerdtham UG, Ruhm CJ. Deaths rise in good economic times:
evidence from the OECD. Econ Hum Biol. 2006;4(3):298–316.
27. Ruhm CJ. A healthy economy can break your heart.
Demography. 2007;44(4):829–848.
28. Ruhm CJ. Macroeconomic conditions, health, and government
policy. In: Schoeni RF, House JS, Kaplan GA, et al, eds.
Making Americans Healthier: Social and Economic Policy as
Health Policy. New York, NY: Russell Sage Foundation;
2008:173–200.
29. Tapia Granados JA. Macroeconomic fluctuations and mortality
in postwar Japan. Demography. 2008;45(2):323–343.
30. Tapia Granados JA. Increasing mortality during the expansions
of the US economy, 1900–1996. Int J Epidemiol. 2005;34(6):
1194–1202.
31. Dehejia R, Lleras-Muney A. Booms, busts, and babies’ health.
Q J Econ. 2004;119(3):1091–1130.
32. Chay KY, Greenstone M. The impact of air pollution on infant
mortality: evidence from geographic variation in pollution shocks
induced by a recession. Q J Econ. 2003;118(3):1121–1167.
33. Miller DL, Page ME, Stevens AH, et al. Why are recessions
good for your health? Am Econ Rev. 2009;99(2):122–127.
34. Stevens AH, Miller DL, Page ME, et al. The Best of Times, the
Worst of Times: Understanding Pro-Cyclical Mortality. (NBER
working paper w17657). Cambridge, MA: National Bureau of
Economic Research; 2011.
35. French MT, Gumus G. Macroeconomic fluctuations and
motorcycle fatalities in the U.S. Soc Sci Med. 2014;104:
187–193.
36. Regidor E, Barrio G, Bravo MJ, et al. Has health in Spain been
declining since the economic crisis? J Epidemiol Community
Health. 2014;68(3):280–282.
37. Meer J, Miller DL, Rosen HS. Exploring the health–wealth
nexus. J Health Econ. 2003;22(5):713–730.
38. Lillard LA, Panis CWA. Marital status and mortality: the role of
health. Demography. 1996;33(3):313–327.
39. Condliffe S, Link CR. The relationship between economic
status and child health: evidence from the United States. Am
Econ Rev. 2008;98(4):1605–1618.